We’re thrilled to announce that Cube Cloud has been recognized by DBTA as one of the Trend-Setting Products in Data and Information Management for 2025! 🎉 Check it out 👉 https://hubs.la/Q02-Bjrz0
Cube
Software Development
The semantic layer for building powerful, fast, and consistent data applications
About us
Cube is the universal semantic layer that makes it easy to connect data silos, create consistent metrics, and make them accessible to all of your BI tools, customer-facing embedded analytics, as well as LLMs, AI chatbots, and agents. Cube is the company behind the wildly popular Cube open source project and delivers the Enterprise-ready Cube Cloud that includes additional functionality - such as integrations with Power BI, Tableau, and Looker - along with robust developer tools, observability, security, and compliance making it easy to quickly deploy, monitor, and use Cube across any sized business. Companies such as Drift, Cloud Academy, Security Scorecard, Intuit, Walmart and IBM trust Cube to deliver amazing data experiences to their customers and employees. Cube is supported by investors such as Bain Capital and Decibel and is located in San Francisco, CA.
- Website
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https://cube.dev
External link for Cube
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- San Francisco
- Type
- Privately Held
- Founded
- 2019
- Specialties
- Analytics, Databases, Developer Tools, Open Source, Business Intelligence, Embedded Analytics, LLMs, APIs, Caching, Query Performance, and Semantic Layer
Locations
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Primary
San Francisco, US
Employees at Cube
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Nnamdi Okike
Managing Partner and Co-Founder at 645 Ventures. Focused on investing in exceptional early-stage software and software-enabled companies.
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Glenn Price
Sales Leader - Central US & Canada
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Annie Teng
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Michael Proia
Technology Sales Leader | Early Stage Startup Expertise | Advisor
Updates
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Join us for an upcoming webinar with Arynn Martin-Post, Enhancing Query Performance with Pre-Aggregations and Caching Layer in Cube 📅 Thursday, December 11 at 9 am PT | 12 pm ET Discover how to achieve peak query performance with Cube's pre-aggregations and in-memory caching layers. During this session, we’ll explore best practices and hands-on techniques to: ✨ Minimize database load and improve scalability with pre-aggregations ✨ Boost query speeds with Cube's intelligent caching mechanism ✨ Design the right data refresh strategies to balance speed and accuracy ✨ Inspect, monitor, and optimize queries like a pro Get ready for a live demo and a Q&A session to help you apply these techniques instantly. 👉 Don’t miss the chance to enhance your data stack and deliver fast user analytics experiences. Register now: https://lnkd.in/gpCKdpaH
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Ready to uplevel your analytics game? It starts here—at "Building Better Data Experiences: Embedded Analytics Deep Dive!" 🔗 Register now and join us for this session 👉 https://hubs.la/Q02ZtxcG0
Building Better Data Experiences: Embedded Analytics Deep Dive
cube.registration.goldcast.io
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AWS re:Invent is here! Come by booth #691 or meet with the Cube crew 🔗https://hubs.la/Q02YLtD80
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Cube reposted this
Excited to be in Las Vegas this week with the Cube team for AWS re:Invent! 🌟 This year, the AWS Solutions Architecture team is hosting a workshop on how to use Cube with AWS data sources. Mark your calendars: 🗓 Thursday, Dec 5 ⏰ 12:30 PM 📍 MGM Grand, Level 3, Premier 311 If you're here at re:Invent, stop by our booth or DM me—I’d love to catch up and chat about all things data and semantic layers!
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Our upcoming webinar, "Building Better Data Experiences: Embedded Analytics Deep Dive," is your inside pass to unlocking the potential of modern embedded analytics. Here's what you'll gain from attending: ✨ Benefits Galore: Discover why integrating analytics directly into your applications is the game-changer for creating unforgettable user experiences. ☁️ Cloud Revolution: Understand how cloud technology powers innovation in the embedded analytics space. 🛠 Tools That Matter: Learn how to pick the perfect tools for your project needs—because one size never fits all. 📦 Your Data Stack Blueprint: Get an in-depth understanding of the key components of a modern, future-ready embedded analytics setup. This is your moment to stay ahead in analytics and future-proof your strategies. Don't miss out— Reserve your spot today! https://hubs.la/Q02ZtKyw0
Building Better Data Experiences: Embedded Analytics Deep Dive
cube.registration.goldcast.io
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Are you ready to uplevel the way your users interact with data? Join us for an exclusive deep dive webinar: "Building Better Data Experiences: Embedded Analytics Deep Dive" 🌟 Understanding and leveraging the modern data stack is no longer optional—it's essential. This webinar will unlock the secrets to embedding analytics seamlessly into your applications, amplifying your user experience like never before. 🎯 What’s in it for you? Discover how cloud technology is reshaping the embedded analytics landscape and learn to choose the right tools that align with your unique project goals. Take advantage of this opportunity to elevate your data strategy. 🗓️ Mark your calendar and secure your spot today! Sign up now to take a step closer to mastering embedded analytics 👉 https://hubs.la/Q02Ztxcz0
Building Better Data Experiences: Embedded Analytics Deep Dive
cube.registration.goldcast.io
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🌟 Calling all AWS enthusiasts! Join us for Data Rock and Roll at the Brooklyn Bowl! Connect with innovators, engage in discussions about cloud and data, and compete in friendly bowling matches. It’s the perfect mix of fun and networking! 🎶🍻 🗓️ Tuesday, December 3rd 🕒 6:00pm - 10:00pm 🔗 https://hubs.la/Q02YLD8R0 #Bowling #Networking #AWSreInvent
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Cube reposted this
Data modeling is one of the most essential tasks. It's where business requirements meet engineering, forming the foundation of any data project. But why don't we take more care of it? Why did we write the same metrics differently across departments? Why do we keep reinventing data models with each new tool we adopt? This got me thinking: Wouldn't it be convenient to separate the presentation from the storage through a data modeling layer? A place where we can declaratively define our metrics, measures, and business KPIs to update them confidently and version them. Today, we have the beginning of such a separation of concerns with a Semantic Layer, and I make my case in this article (https://lnkd.in/ew-etp6u). --- In case you have never heard or didn't fully understand what a semantic layer is and why you should have one, this is my article. I compare it to the MVC (Model View Controller), a term popularized in the 70s and used to this day. I wondered why and took parallels between the data world and MVC, including Active Records, the intermediary between presentation (view) and data source (model). Translating and implementing valuable business logic, the ORM maps the code to its database table. Wouldn't it be nice to have an *ORM for data*, too? A modeling language that defines metrics, facts, and dimensions and applies them to the model, the heterogeneous data sources. This allows for better maintainability, separation of concerns, and, above all, the ability to model the data logically to map the business requirements to our databases. Additionally, we get even more: 🎨 #DataModeling: Unified, consistent metric definition in a declarative manner. 📈 #APIs (GraphQL, REST, SQL, MDX, AI): Integrate with any endpoint and deliver trusted data. 👮🏻 #AccessControl: Centralize and fine-grain governance and security policies. 🏃🏻♂️ #Caching: Query optimization, pre-aggregations; deliver faster, more cost-efficient results. --- We can use the DRY principle, create complex business KPIs once, and provide convenient access through out-of-the-box APIs. We can reuse and build on top of each measure and business metric. Ultimately, it enables better data governance and simplifies data modeling in large organizations. Read more in the article; I hope you enjoy it. I'm excited to hear what you think about this comparison.
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Cube reposted this
Data modeling is one of the most essential tasks. It's where business requirements meet engineering, forming the foundation of any data project. But why don't we take more care of it? Why did we write the same metrics differently across departments? Why do we keep reinventing data models with each new tool we adopt? This got me thinking: Wouldn't it be convenient to separate the presentation from the storage through a data modeling layer? A place where we can declaratively define our metrics, measures, and business KPIs to update them confidently and version them. Today, we have the beginning of such a separation of concerns with a Semantic Layer, and I make my case in this article (https://lnkd.in/ew-etp6u). --- In case you have never heard or didn't fully understand what a semantic layer is and why you should have one, this is my article. I compare it to the MVC (Model View Controller), a term popularized in the 70s and used to this day. I wondered why and took parallels between the data world and MVC, including Active Records, the intermediary between presentation (view) and data source (model). Translating and implementing valuable business logic, the ORM maps the code to its database table. Wouldn't it be nice to have an *ORM for data*, too? A modeling language that defines metrics, facts, and dimensions and applies them to the model, the heterogeneous data sources. This allows for better maintainability, separation of concerns, and, above all, the ability to model the data logically to map the business requirements to our databases. Additionally, we get even more: 🎨 #DataModeling: Unified, consistent metric definition in a declarative manner. 📈 #APIs (GraphQL, REST, SQL, MDX, AI): Integrate with any endpoint and deliver trusted data. 👮🏻 #AccessControl: Centralize and fine-grain governance and security policies. 🏃🏻♂️ #Caching: Query optimization, pre-aggregations; deliver faster, more cost-efficient results. --- We can use the DRY principle, create complex business KPIs once, and provide convenient access through out-of-the-box APIs. We can reuse and build on top of each measure and business metric. Ultimately, it enables better data governance and simplifies data modeling in large organizations. Read more in the article; I hope you enjoy it. I'm excited to hear what you think about this comparison.